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Bioinformatics. 2008 Nov 15;24(22):2615-21. doi: 10.1093/bioinformatics/btn500. Epub 2008 Sep 19.

Can sugars be produced from fatty acids? A test case for pathway analysis tools.

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  • 1Department of Bioinformatics, Friedrich-Schiller-Universit├Ąt Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany. ldpf@minet.uni-jena.de;

Corrected and republished in

Abstract

MOTIVATION:

In recent years, several methods have been proposed for determining metabolic pathways in an automated way based on network topology. The aim of this work is to analyse these methods by tackling a concrete example relevant in biochemistry. It concerns the question whether even-chain fatty acids, being the most important constituents of lipids, can be converted into sugars at steady state. It was proved five decades ago that this conversion using the Krebs cycle is impossible unless the enzymes of the glyoxylate shunt (or alternative bypasses) are present in the system. Using this example, we can compare the various methods in pathway analysis.

RESULTS:

Elementary modes analysis (EMA) of a set of enzymes corresponding to the Krebs cycle, glycolysis and gluconeogenesis supports the scientific evidence showing that there is no pathway capable of converting acetyl-CoA to glucose at steady state. This conversion is possible after the addition of isocitrate lyase and malate synthase (forming the glyoxylate shunt) to the system. Dealing with the same example, we compare EMA with two tools based on graph theory available online, PathFinding and Pathway Hunter Tool. These automated network generating tools do not succeed in predicting the conversions known from experiment. They sometimes generate unbalanced paths and reveal problems identifying side metabolites that are not responsible for the carbon net flux. This shows that, for metabolic pathway analysis, it is important to consider the topology (including bimolecular reactions) and stoichiometry of metabolic systems, as is done in EMA.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

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PMID:
18806269
[PubMed - indexed for MEDLINE]
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